Biometry techniques are used for the detection and recognition of living beings. These techniques can be used in the context of applications requiring a certain level of security, such as, for example, access control at sensitive sites.
For this, a morphological analysis applied to the individuals is implemented in order to identify the physical characteristics that are specific to them. This analysis is based, for example, on the iris or the fingerprints.
For the analysis of the iris, an example of an existing analysis method is the so-called Daugman method, described in the U.S. Pat. No. 5,291,560. This method allows for the comparison between a number of digital samples representative of irises and then makes it possible to determine whether the samples correspond to the same individual. For this, there is a first step whose aim is to segment and normalize the irises followed by a step aiming to extract a binary code. The extraction of the binary code is done by applying a phase demodulation around points of application to transform the texture of the iris into a binary code. The comparison of two irises is therefore reduced to a point-by-point comparison of a number of binary codes, in which the points of the binary codes were directly associated with points of application placed on the normalized iris.
The positioning of the points of application on the normalized image of the iris can be done differently. The Daugman method as described in the U.S. Pat. No. 5,291,560 proposes to position the points of application in the left and right quadrants of the iris. The aim of this positioning is to exclude the regions of the iris in which the probability of containing artifacts is significant. This is because certain regions may contain eyelashes, eyelids or light spots. By excluding these regions, the inclusion of noise-affected information in the binary code, and therefore of their comparison being falsified, is avoided. The drawback with this approach is that the positions of the points of application are predefined and identical for all the iris images. They do not therefore allow for adaptation to the specifics of each iris.
To address this problem, L. Masek proposed, in his thesis entitled “Recognition of Human Iris Patterns for Biometric Identification”, 2003, introducing a segmentation mask on the normalized iris. This mask is computed automatically for each iris by an active contours method. The aim of this mask is to cover the artifacts present in the iris. The points of application taken into account for the comparison of the irises are then placed in unmasked regions. However, this technique has limits because it is binary and uniformly processes all the unmasked regions. Thus, the regions that are highly textured or have little texture as well as the regions containing unmasked artifacts or not containing any are processed in the same way. Hereinafter in the description, the words “region” or “area” will be used to designate a normalized iris image portion.
It was then proposed to locally measure the quality in different regions of the iris and to use a weighting by these quality measurements at the moment of the comparison of these binary codes of the irises. In this description, a quality measurement corresponds, for example, to an estimation of the level of texture of a given region of the iris and its resemblance to an iris texture. This is what is described in the article by Y. Chen et al. entitled Localized Iris Image Quality Using 2-D Wavelets, proceeding of international conference on biometrics, Hong-Kong, China, 2006. The quality measurement is also explained in the article by E. Krichen, S. Garcia-Salicetti and B. Dorizzi entitled A new probabilistic Iris Quality Measure for comprehensive noise detection, IEEE First International Conference on Biometrics: Theory, Applications and Systems, Washington USA, September 2007.
The abovementioned approaches do, however, have drawbacks. For example, the quality measurement presented in the article by Y. Chen et al. does not allow for processing of the artifacts, unlike that of Krichen. As for the quality measurement in the article by E. Krichen et al., this is not robust when used to implement a weighting in the comparison of binary codes and to do so, for example, when the regions of the iris are of very poor quality, notably when these regions have little texture. Indeed, this quality measurement may allocate very low quality values to regions of the iris if these regions have little texture. Now, some irises intrinsically include a significant proportion of regions with little texture. Weighting these regions by low quality scores amounts to reducing the quantity of information available for the comparison, which skews the result of said comparison.
Note: conversely, the method that we are proposing requires N points to be taken into account for the comparison even for irises with little texture, which prevents having skewed comparison results.
Other iris recognition techniques not based on the Daugman system do also exist. Take, for example, the case of the system based on the correlation described in the patent EP 08788209. Although this last solution is more robust to degraded iris images than the Daugman system, it is much more complex and costly in computation time.